Introduction to Probabilistic Graphical Models

Dr. L. Enrique Sucar, Ciencias Computacionales, INAOE

Probabilistic graphical models have become a powerful set of techniques used in several domains. This course provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It covers the fundamentals of the main classes of PGMs: Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, Markov decision processes, partially observable Markov decision processes and relational and causal PGMs; including representation, inference, and learning principles for all the techniques. Realistic applications for each type of model are also covered.

Curso de Matemáticas Discretas del propedéutico para ingreso al posgrado de Maestría en Ciencias Computacionales

Temario:

·         Introducción

·         Conjuntos

·              Relaciones y funciones

·              Inducción y recursión

·              Grafos

·              Lógica

·              Probabilidad